Faster and More Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization

Core Concepts

LightningSimV2 achieves up to 3.5× faster full simulation and up to 577× faster incremental design space exploration compared to the previous LightningSim tool, by employing static analysis to reduce redundant computation, a novel graph-based simulation approach, and an efficient incremental design space exploration technique.

Abstract

The paper introduces LightningSimV2, a faster and more scalable simulation tool for High-Level Synthesis (HLS) designs compared to the previous LightningSim tool. Key innovations: Optimized trace generation: LightningSimV2 employs static analysis to identify repetitive design patterns, such as loops, and avoids tracing every iteration, generating much smaller execution traces. Optimized and decoupled stall calculation: Instead of the event-based simulation in LightningSim, LightningSimV2 adopts a novel graph-based simulation technique. It decouples the stall calculation into two steps: dynamic simulation graph construction and graph traversal. This achieves up to 6.4× speedup for the trace analysis step. Optimized and parallelizable design space exploration (DSE): The decoupled graph-based simulation enables efficient DSE for HLS dataflow designs. The simulation graph can capture unknown dependencies during construction, so only the lightweight graph traversal step needs to be executed when performing DSE for parameters like FIFO depths. This enables high-throughput parallel evaluation of multiple design points. Evaluation results show that LightningSimV2 achieves up to 3.5× speedup in full simulation and up to 577× speedup for incremental DSE compared to LightningSim, particularly for large designs. The code is open-source on GitHub.

Stats

LightningSimV2 achieves up to 3.5× speedup in full simulation compared to LightningSim. LightningSimV2 achieves up to 577× speedup for incremental design space exploration compared to LightningSim.

Quotes

"LightningSimV2 features three main innovations. First, we perform compile-time static analysis, exploiting the repetitive structures in HLS designs, e.g., loops, to reduce the simulation workload. Second, we propose a novel graph-based simulation approach, with decoupled simulation graph construction step and graph traversal step, significantly reducing repeated computation. Third, benefiting from the decoupled approach, LightningSimV2 can perform incremental stall analysis extremely fast, enabling highly efficient design space exploration of large numbers of complex hardware parameters, e.g., optimal FIFO depths."

Key Insights Distilled From

LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization

by Rishov Sarka... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09471.pdf

Deeper Inquiries

How can the proposed graph-based simulation approach be extended to handle other types of hardware dependencies beyond FIFO read/write and AXI memory accesses

The proposed graph-based simulation approach can be extended to handle other types of hardware dependencies by introducing new edge types and tracking mechanisms in the simulation graph. For example, dependencies related to control signals, data dependencies between modules, and synchronization signals can be modeled using additional edge types in the graph. By incorporating these new edge types and updating the graph traversal algorithm to consider these dependencies, the simulation tool can accurately capture and simulate a wider range of hardware interactions. Additionally, introducing specialized trackers for different types of dependencies, similar to the FIFO and AXI trackers, can help manage and resolve these dependencies efficiently during graph construction and traversal.

What are the potential limitations or challenges in applying the static analysis techniques to identify repetitive design patterns in more complex HLS designs

Applying static analysis techniques to identify repetitive design patterns in more complex HLS designs may face several limitations and challenges. One potential limitation is the increased complexity and variability of design patterns in larger and more intricate designs, making it challenging to accurately identify and optimize all instances of repetitive patterns. Additionally, the interplay between different modules, data dependencies, and control flow in complex designs can make it harder to isolate and optimize specific patterns without impacting the overall functionality of the design. Moreover, the scalability of static analysis techniques to handle the increased size and complexity of larger designs may pose a challenge, requiring more sophisticated algorithms and computational resources to perform efficient pattern recognition and optimization.

Could the simulation graph construction and traversal approach be further optimized or parallelized to achieve even higher performance for large-scale HLS designs

The simulation graph construction and traversal approach can be further optimized and parallelized to achieve even higher performance for large-scale HLS designs. One way to optimize the process is by implementing parallel graph construction techniques, where different parts of the graph can be constructed concurrently by multiple threads or processes. This parallelization can significantly reduce the overall graph construction time, especially for designs with a large number of modules and events. Additionally, optimizing the graph traversal algorithm for parallel execution on multi-core processors can further enhance the performance of the simulation tool. By efficiently distributing the traversal tasks across multiple cores and utilizing parallel processing techniques, the tool can achieve faster cycle count calculations and improved scalability for large-scale HLS designs.